Bibliographic record
Abstract
CJ: Janette Turner Hospital is one of Australia's finest writers. She has produced eight literary novels, one crime novel--published under the pseudonym Alex Juniper--and four collections of short stories. Born in Melbourne, she spent her formative years in Brisbane where she was educated, studying for her first degree at the University of Queensland. Here she met her husband, Cliff Hospital, and travelled overseas with him, living in India, England, Canada and America. Currently she is Carolina Distinguished Professor of English at the University of South Carolina--quite a leap from a Primary School in Brisbane ... JTH: True, via Canada, England, India ... CJ: And your husband is here today, looking as though he could be one of the heroes from your books ... has he ever appeared in your work? JTH: No one in real life appears directly, but most characters have points of origin in real life. They're usually accretions of several real life people's components ... so I would say there are traces of him here and there. CJ: Can you tell me where? JTH: More in the short stories, actually; I was always fascinated by a story in Cliff's family.... When his grandparents left England, they had a baby, and their parents gave them a gold coin, a half-sovereign, I think it was. They put it in the baby's hand. That baby died and was buried at sea on the voyage out, and another baby was born on the voyage out. And so the short story, 'Uncle Seaborn' ... Seaborn was the name given to the baby born on the voyage and this gold coin came down through the family to Cliff's sister and I've always been fascinated by that story. Particularly the hardships in women's lives.... Can you imagine? In a nine-month sea voyage a baby dies and another baby is born. It just boggles the mind. No matter how rough we women have had it in our time, nothing could compare with that. CJ: All of your books feature fabulous women who cross borders, are independent and creative. Now, do you have a favourite amongst your heroines? JTH: Oh ... all the feisty ones, I think. And that's because I wish I had that degree of independence and guts ... CJ: I find that rather amusing--that you wish you had that feistiness, because you're certainly a feisty writer. And this is why so many women passionately love your writing; and so many more young women, who haven't yet read your opus will passionately love it. JTH: Well, thank you Cheryl. In actual truth, though, in my High School group of six close friends--four women and two men--girls and boys in High School days--two of the women were suicides in their twenties, all of us, for various reasons, felt quite vulnerable in a way.... We were the first in all of our families to finish High School, even, let alone go to university. And, there was always a great fear, that I would be one of the ones who would go under ... CJ: Who had the fear? JTH: Who had the fear? I did. CJ: When you say, 'go under' ... do you mean that you thought you would commit suicide? JTH: Not exactly that. I mean, I never felt that I would be at risk of suicide, but those of us who were breaking new ground were pretty much alone in our fields. There wasn't much back-up, there were a lot of patronising male put-downs, so I was very aware that you might go under--or you had to be very, very tough to make sure you didn't. And so, most of the female characters in my novels are actually bifurcated, in that there's one who doesn't make it and one who does. There are three figures who, I guess, represent aspects of myself and the one in the middle was the sane, normal one. But I always feared that I might fall under, and to safeguard against that I'd better be daring and always face down fears. Which has been my path, actually, so I am delighted to find that you think it funny that I don't think myself feisty, when I've always been afraid of the other extreme. CJ: Well, you always learn something unexpected about writers when you talk to them. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.070 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".